• Understand the current debates around innovation in the digital economy, strategy, as it relates to digital economy and data.
• Explain how digital changes and the availability of data transform the business landscape.
• Discuss relevant theories and explain their assumptions, causal dynamics and processes.
• Assess the role of data in business innovation and specify success and failure factors.
• Understand central concepts around management in the context of digital businesses.
• Discuss ethical issues of the digital economy and organizations’ use of data.

This course will give students an introduction to central issues of innovation and strategy in the digital economy. It forms the background for the study of data in the business context. The growth of the digital economy is predicting a data rich future and a transformation of the business landscape with implications for existing firm, entrepreneurial ventures, and individual workers. In some of the most dynamic sectors of the modern economy, such as, apps for smartphones, video games, media content, scientific and technical problems solving, companies’ overall performance already rely on data, setting requiring a whole new set of skills and organizational capabilities. The course will develop the conceptual foundations, frameworks and methods for analyzing the relationships between communities, crowds, and firms. It introduces student to the process of digital transformation. The course gives students a systematic basis for assessing the economic potential of different sorts of data and organizational imperatives to unlocking that potential. The first part of the course introduces business models of the digital economy. The latter part will focus on organizing in a data rich business context. Topics will include:

• Digital economy and innovation
• Digital business models and strategy
• The role of crowds in generating innovation and predictions.
• Crowdsourcing, crowd funding, collaborative innovation
• Digital platforms
• Managing organizations in a data rich future
• Data networks and markets
• Assessment of data potential

This course is part of the minor in big data

• Brynjolfsson, Erik and Geva, Tomer and Reichman, Shachar, Crowd-Squared: Amplifying the Predictive Power of Large-Scale Crowd-Based Data (October 22, 2014). Available at SSRN: http://ssrn.com/abstract=2513559

• Burtch, G., Ghose, A, S Wattal 2013, An Empirical Examination of the Antecedents and Consequences of Contribution Patterns in Crowd-Funded Markets, Information Systems Research 24 (3), 499-519

• Chen, H., Chiang RHL. VC. Storey, 2012, Business Intelligence And Analytics: From Big Data To Big Impact, MIS Quarterly 36 (4), pp. 1165-1188

• Edelman, B., 2012, Using Internet Data for Economic Research, Journal of Economic Perspectives 26 (2) pp. 189–206 (*)

• Franzoni C., Sauermann H. Crowd Science: The Organization of Scientific Research in Open Collaborative Projects, Research Policy

• Greenstein, S., Lerner J., S Stern, 2013 Digitization, innovation, and copyright: What is the agenda? Strategic Organization, (11) 110. 110-121

• Jeppesen, L.B. and Lakhani, K.R., (2010) Marginality and Problem Solving Effectiveness in Broadcast Search, Organization Science, 21 (5) 1016-1033

• Varian, H. R., Computer Mediated Transactions, March 6, 2010; http://people.ischool.berkeley.edu/~hal/Papers/2010/cmt.pdf

• von Hippel, (2005) Democratizing Innovation, MIT Press, Chapter 1

• Yoo Y, Boland R, Lyytinen K, Majchrzak A. 2012. Organizing for innovation in a digital world. Organization Science 23(5):1398–1408.